Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system for monitoring the health of a person, the system comprising: at least one sensor capable of measuring at least one physiological signal generated by an autonomic nervous system of the person for providing measurement data, and a data processing system configured to store a reference indicator, receive measurement data from the sensor during a plurality of consecutive health-maintenance sessions in order to collect a plurality of measurement data sets corresponding to said health-maintenance sessions, determine at least one first health indicator based on the measurement data sets, said at least one first health indicator being sensitive to a physiological state of or changes in the autonomic nervous system of the person, and compare the at least one first health indicator with said reference indicator, wherein the at least one first health indicator comprises a plurality of second health indicators separately determined based on each measurement data set and representing temporal variation of measurement data within said measurement data sets, and the data processing system is configured to calculate the second health indicators based on first measurement data contained in the first half of a measurement data set, corresponding to the first half of a single health-maintenance session, and second measurement data contained in the second half of a measurement data set, corresponding to the second half of the single health-maintenance session, wherein the data processing system is configured to store a health-monitoring session plan which is changed based on the comparison of the at least one first health indicator with the reference indicator, wherein the health maintenance-session is a single meditation session, a single exercise session, or a single controlled breathing session.
The system monitors a person's health by analyzing physiological signals from the autonomic nervous system. At least one sensor measures these signals during health-maintenance sessions, such as meditation, exercise, or controlled breathing, generating measurement data. A data processing system collects multiple data sets from consecutive sessions and calculates health indicators sensitive to autonomic nervous system states or changes. These indicators include temporal variations within each session, derived by comparing data from the first and second halves of a session. The system compares these indicators to a stored reference value and adjusts a health-monitoring session plan based on the comparison. The goal is to track and adaptively manage the person's health by analyzing autonomic nervous system activity over time.
2. The system according to claim 1 , wherein the at least one first health indicator comprises a plurality of health indicators separately determined based on each measurement data set and representing a state of the autonomic nervous system.
3. The system according to claim 1 , wherein the at least one first health indicator comprises a health trend indicator determined based on a plurality of measurement data sets from a plurality of health-maintenance sessions.
This invention relates to a health monitoring system that analyzes health data from multiple sessions to determine trends in a user's health status. The system collects measurement data sets during health-maintenance sessions, such as fitness activities, medical check-ups, or wellness routines. These data sets include physiological metrics like heart rate, blood pressure, or activity levels. The system processes the data to generate a health trend indicator, which reflects changes in the user's health over time. This trend indicator helps identify improvements, deterioration, or stability in health conditions, enabling personalized health management. The system may also include additional health indicators, such as real-time alerts or comparative benchmarks, to provide a comprehensive health assessment. By analyzing multiple data sets across sessions, the system offers a more accurate and dynamic understanding of a user's health trajectory compared to single-session measurements. This approach supports proactive health interventions and long-term wellness planning.
4. The system according to claim 3 , wherein said measurement data sets comprise session time values and the data processing system is configured to calculate the health trend indicator using said session time values and measurement data, whereby the health trend indicator is proportional to the rate of change of the said physiological changes of autonomic nervous system.
This invention relates to a system for monitoring and analyzing physiological changes in the autonomic nervous system (ANS) to assess health trends. The system collects measurement data sets, including session time values and physiological measurements, to evaluate ANS activity. The data processing system calculates a health trend indicator based on these values, where the indicator is proportional to the rate of change of ANS physiological changes over time. This allows for tracking the progression or improvement of health conditions related to autonomic function. The system may also include a user interface for displaying the health trend indicator and other relevant data, enabling users to monitor their health status and detect trends in autonomic nervous system activity. The invention is particularly useful for applications in healthcare, fitness, and wellness, where continuous monitoring of ANS function can provide insights into overall health and stress levels. By analyzing the rate of change in physiological measurements, the system provides a quantitative assessment of health trends, aiding in early detection of potential health issues and guiding personalized health interventions.
5. The system according to claim 1 , wherein the data processing system is configured to determine a plurality of health indicators based on each measurement data set separately and associate a health-maintenance session time value to the health indicators determined, in said comparison, to determine if the health indicators have changed at a predefined rate using the reference indicator and the health-maintenance session time values.
6. The system according to claim 5 , wherein the data processing system is configured to determine a temporal health trend indicator based on the separate health indicators and said time values associated therewith.
The system relates to health monitoring and trend analysis, addressing the need to track and evaluate changes in an individual's health status over time. The system processes health data to generate separate health indicators, which are quantitative or qualitative measures of specific health parameters such as vital signs, biomarkers, or activity levels. Each health indicator is associated with a time value, representing when the measurement was taken. The system then analyzes these indicators and their corresponding time values to determine a temporal health trend indicator. This trend indicator reflects how the individual's health status evolves over time, identifying patterns, improvements, or deteriorations. The system may also include components for collecting health data from wearable devices, medical sensors, or user inputs, and for storing and organizing the data for analysis. The temporal health trend indicator can be used to support clinical decision-making, personalized health recommendations, or early detection of health issues. The system ensures continuous and dynamic monitoring of health trends, enabling proactive health management.
7. The system according to claim 6 , wherein the data processing system is configured to collect at least one pre-monitoring data set during at least one first pre-monitoring health session before said health-maintenance sessions, use the pre-monitoring data set for determining said reference indicator.
The invention relates to a health monitoring system designed to collect and analyze health data over time to support health maintenance. The system addresses the challenge of establishing a baseline for health indicators to detect deviations and trends, enabling early intervention and personalized health management. The system includes a data processing component that collects at least one pre-monitoring data set during one or more initial health sessions before regular health-maintenance sessions begin. These pre-monitoring sessions serve as a baseline reference. The collected data is used to determine a reference indicator, which represents a baseline health state. This reference indicator is then used during subsequent health-maintenance sessions to compare against new data, allowing the system to track changes in health status over time. The system may also include sensors or devices to gather physiological data, such as vital signs, activity levels, or biomarkers, and may integrate this data with user inputs or external sources to provide a comprehensive health assessment. By establishing a reference indicator from pre-monitoring data, the system can more accurately identify deviations from normal health patterns, supporting proactive health management and personalized recommendations.
8. The system according to claim 7 , wherein said using of the pre-monitoring data set comprises at least one of: determining a pre-monitoring health indicator based on the at least one pre-monitoring data set and computing and storing the reference indicator based on the pre-monitoring health indicator, sending the pre-monitoring data or any value derived therefrom over a data network, and receiving the reference indicator associated with the pre-monitoring data over a data network.
9. The system according to claim 1 , wherein the data processing system is further configured to perform at least one of: store the result of comparison of the at least one health indicator and the reference indicator, display the result of comparison of the at least one health indicator and the reference indicator, communicate over a data network the result of comparison of the at least one health indicator and the reference indicator.
10. The system according to claim 1 , wherein the data processing system is further configured to store a session plan capable of containing session data including the timing and content of said health-maintenance sessions, and associate at least one of: said measurement data sets, health indicators, with said session data after said comparison of the at least one first health indicator with said reference indicator, to allow for changing of the session plan.
11. The system according to claim 1 , wherein the at least one sensor comprises a heart rate (HR) sensor, the at least one physiological signal comprises a heart rate signal, and the at least one health indicator comprises a heart rate variability (HRV) index.
12. The system according to claim 1 , wherein the at least one sensor comprises an electroencephalographic (EEG) sensor, the at least one physiological signal comprises an EEG signal, and the at least one health indicator comprises a brain activity index.
13. The system according to claim 1 , further comprising a smartphone for providing auditory feedback, for the person during the measurement of the physiological signal based on the measurement data.
14. The system according to claim 1 , wherein the data processing system comprises a storage medium comprising software executable on a mobile computing device, the first software means being functionally connectable with said sensor, and wherein the software comprises computer-executable instructions for performing said step of receiving.
This invention relates to a data processing system for mobile computing devices, specifically addressing the challenge of efficiently receiving and processing sensor data from external sensors. The system includes a storage medium containing software executable on a mobile device, which is functionally connectable to a sensor. The software contains computer-executable instructions for receiving sensor data, enabling the mobile device to interface with and process inputs from various external sensors. The system ensures seamless integration between the mobile device and the sensor, allowing for real-time data acquisition and analysis. This functionality is particularly useful in applications requiring portable, on-device processing of sensor inputs, such as environmental monitoring, health tracking, or industrial diagnostics. The software's ability to receive and manage sensor data directly on the mobile device eliminates the need for additional processing hardware, enhancing portability and reducing latency. The system is designed to support a wide range of sensors, ensuring versatility across different use cases. By embedding the sensor data reception functionality within the mobile device's software, the invention provides a scalable and efficient solution for mobile sensor data processing.
15. The system according to claim 14 , wherein the software further comprises computer-executable instructions for providing visual or auditory guidance for the person on conducting the health-maintenance sessions.
16. The system according to claim 14 , wherein the data processing system further comprises second software executable on a network data server, and the first and second software are capable of exchanging data over a network connection, and the second software comprises computer-executable instructions for performing at least one of the steps of storing, determining and comparing.
17. The system according to claim 14 , wherein the data processing system further comprises user access control software, wherein said user access control software is capable of containing at least a first and second user access levels, wherein the first access level authorizes the person to initiate data collection for said health-maintenance sessions, and the second user access level authorizes another person to define said reference indicator and/or to set a session guidance for the person conducting the health-maintenance sessions.
18. The system according to claim 17 , wherein the second user access level authorizes access to at least one of: the measurement data sets, the health indicators, the result of the comparison via a computer network.
19. The system according to claim 14 , wherein the data processing system is further configured to collect and analyze at least one of: measurement data sets, health indicators, from health-maintenance sessions of a plurality of different persons for obtaining population data, and utilize the population data for determining the reference indicator.
20. A method of monitoring the health of a person, the method comprising determining a session plan and storing the session plan in a non-transitory form within a data processing system, and using at least one sensor capable of measuring at least one physiological signal generated by a autonomic nervous system of the person, measuring at least one physiological signal generated by the autonomic nervous system of the person during a plurality of consecutive health-maintenance sessions according to the session plan stored in a data processing system, and collecting into the data processing system a plurality of measurement data sets corresponding to said health-maintenance sessions, and operating the data processing system for determining at last one first health indicator based on the measurement data sets, said at least one first health indicator being sensitive to physiological state of or changes in the autonomic nervous system, and comparing the at least one first health indicator against predefined comparison criteria, changing the session plan based on the outcome of the comparison, wherein the at least one first health indicator comprises a plurality of second health indicators separately determined based on each measurement data set and representing temporal variation of measurement data within said measurement data sets, and the data processing system is operated to calculate the second health indicators based on first measurement data contained in the first half of a measurement data set, corresponding to the first half of a single health-maintenance session, and second measurement data contained in the second half of a measurement data set, corresponding to the second half of the single health-maintenance session, wherein the health-maintenance session is a single meditation session, a single exercise session, or a single controlled breathing session.
21. The method according to claim 20 , comprising collecting at least four measurement data sets from separate health-maintenance sessions over a period of at least four weeks, such as at least six sets over a period of six weeks.
This invention relates to health monitoring systems that analyze data collected from multiple health-maintenance sessions to assess an individual's health status over time. The problem addressed is the need for reliable, long-term health tracking to detect trends, improvements, or deteriorations in a person's well-being. Traditional health monitoring often relies on single or infrequent measurements, which may not provide an accurate picture of an individual's health trajectory. The method involves collecting at least four measurement data sets from separate health-maintenance sessions over a period of at least four weeks, with a preferred approach being at least six sets over six weeks. Each session generates a data set that includes health-related measurements, such as vital signs, biomarkers, or other physiological indicators. By gathering multiple data sets over an extended period, the system can identify patterns, track progress, and provide more accurate health assessments. The method ensures that the data is collected consistently, allowing for meaningful comparisons and trend analysis. This approach improves the reliability of health monitoring by reducing the impact of short-term fluctuations and providing a more comprehensive view of an individual's health over time. The system may also include additional steps, such as processing the data to generate insights or recommendations based on the collected measurements.
22. The method according to claim 20 , comprising collecting at least one pre-monitoring data set during at least one first pre-monitoring health session before said health-maintenance sessions, determining at least one reference indicator based on the pre-monitoring data set, collecting said measurement data sets, and using the reference indicator as part of said comparison criteria.
23. The method according to claim 20 , wherein the data processing system comprises at least two user accounts having access to monitoring data of the person, the first user account allowing collection and storage of measurement data sets and the second user account allowing reading of measurement data sets and/or health indicators, and the method comprises collecting and storing said data sets using the first user account, reading measurement data sets and/or health indicators derived therefrom using the second user account, and changing the session plan using the second user account.
A data processing system monitors a person's health data, where multiple user accounts manage access to this information. The system includes at least two distinct user accounts: a first account for collecting and storing measurement data sets, and a second account for reading these data sets or derived health indicators. The first account is responsible for gathering and storing physiological or activity measurements, such as heart rate, steps taken, or other health-related metrics. The second account allows authorized users to review the stored data or health indicators, which may include trends, summaries, or alerts based on the raw measurements. Additionally, the second account enables modifications to a session plan, which likely refers to a structured health or fitness routine. This dual-account structure ensures that data collection and data access are managed separately, enhancing security and role-based control. The system may be used in healthcare, fitness tracking, or remote patient monitoring, where different users need varying levels of access to health data. The method ensures that data integrity is maintained while allowing flexible access for different purposes, such as clinical oversight or personal health management.
24. A method of treating autonomic nervous system related dysfunctions of a person, providing at least one sensor capable of measuring at least one physiological signal generated by the autonomic nervous system of the person, providing a data processing system capable of storing and processing measurement data provided by the sensor, operating the sensor and data processing system during a plurality of health-maintenance monitoring sessions according to a session plan for collecting measurement data sets corresponding to said monitoring sessions, determining at last one first health indicator based on the measurement data sets, the at least one first health indicator being sensitive to physiological state of or changes in the autonomic nervous system, and determining if the temporal development of the at least one first health indicator fulfills predefined criteria, and, in the affirmative, changing the session plan, operating the sensor and data processing system during a plurality of further health-maintenance sessions according to the changed session plan for collecting further measurement data sets corresponding to said further sessions wherein the at least one first health indicator comprises a plurality of second health indicators separately determined based on each measurement data set and representing temporal variation of measurement data within said measurement data sets, and the data processing system is operated to calculate the second health indicators based on first measurement data contained in the first half of a measurement data set, corresponding to the first half of a single health-maintenance session, and second measurement data contained in the second half of a measurement data set, corresponding to the second half of the single health-maintenance session, wherein the health-maintenance session is a single meditation session, a single exercise session, or a single controlled breathing session.
The invention relates to a method for treating autonomic nervous system (ANS) dysfunctions by monitoring and adapting health-maintenance sessions. The method involves using at least one sensor to measure physiological signals generated by the ANS, such as heart rate variability or skin conductance. A data processing system stores and analyzes these measurements during multiple health-maintenance sessions, which may include meditation, exercise, or controlled breathing. The system calculates health indicators from the measurement data, with each indicator reflecting ANS activity or changes over time. Specifically, the method determines multiple secondary health indicators for each session by comparing data from the first half and second half of the session. If the temporal development of these indicators meets predefined criteria, the session plan is adjusted to optimize treatment. The system then collects additional data under the modified plan to further assess and refine the treatment approach. This adaptive monitoring allows for personalized and dynamic management of ANS-related dysfunctions.
25. The method according to claim 24 , comprising before operating the sensor and data processing system for the monitoring sessions, operating the sensor and data processing system during at least one pre-monitoring session for collecting at least one pre-monitoring data set, and determining said predefined criteria based on said at least one pre-monitoring data set.
26. The method according to claim 24 , wherein said changing of the session plan comprises changing the timing of sessions, the duration of sessions or the content of the sessions.
This invention relates to adaptive session management systems, particularly for adjusting session plans in response to user behavior or system conditions. The problem addressed is the need for dynamic modification of session parameters to improve efficiency, engagement, or resource utilization. The method involves monitoring user interactions or system performance during sessions and automatically altering the session plan based on predefined criteria. This includes changing the timing of sessions (e.g., scheduling, frequency), the duration of sessions (e.g., shortening or extending), or the content of sessions (e.g., modifying topics, activities, or media). The adjustments are made to optimize outcomes such as user engagement, learning retention, or system load balancing. The system may use historical data, real-time feedback, or predictive analytics to determine the optimal modifications. This approach ensures sessions remain relevant, effective, and aligned with evolving user needs or system constraints.
Unknown
March 30, 2021
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